Hands on Exercise 3

Published

January 28, 2023

Modified

February 26, 2023

1 Installing and Loading relevant R packages

pacman::p_load(sf, spatstat, raster, maptools, tmap)

2 Spatial Data Wrangling

2.1 Import spatial data

childcare_sf <- st_read("data/child-care-services-geojson.geojson") %>%
  st_transform(crs = 3414)
Reading layer `child-care-services-geojson' from data source 
  `/Users/junhaoteo/Documents/junhao2309/IS415/Hands-on_Ex/Hands-on_Ex03/data/child-care-services-geojson.geojson' 
  using driver `GeoJSON'
Simple feature collection with 1545 features and 2 fields
Geometry type: POINT
Dimension:     XYZ
Bounding box:  xmin: 103.6824 ymin: 1.248403 xmax: 103.9897 ymax: 1.462134
z_range:       zmin: 0 zmax: 0
Geodetic CRS:  WGS 84
sg_sf <- st_read(dsn = "data", layer = "CostalOutline")
Reading layer `CostalOutline' from data source 
  `/Users/junhaoteo/Documents/junhao2309/IS415/Hands-on_Ex/Hands-on_Ex03/data' 
  using driver `ESRI Shapefile'
Simple feature collection with 60 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 2663.926 ymin: 16357.98 xmax: 56047.79 ymax: 50244.03
Projected CRS: SVY21
mpsz_sf <- st_read(dsn = "data", layer = "MP14_SUBZONE_WEB_PL")
Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `/Users/junhaoteo/Documents/junhao2309/IS415/Hands-on_Ex/Hands-on_Ex03/data' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

Both mpsz_sf and sg_sf are projecting with SVY21 but are currently using EPSG:9001. We can check by using st_crs().

2.2 Convert data into dataframe

sg_sf <- st_sf(sg_sf) %>%
  st_transform(crs = 3414)
mpsz_sf <- st_sf(mpsz_sf) %>%
  st_transform(crs = 3414)

2.3 Mapping geospatial datasets

tmap_mode("plot")
qtm(mpsz_sf) + 
  qtm(childcare_sf)

tmap_mode("plot")

Alternative given by prof:

tmap_mode("view")
tm_shape(childcare_sf) +
  tm_dots()

3 Geospatial Data wrangling

3.1 Converting sf data frames to sp’s Spatial class

childcare <- as_Spatial(childcare_sf)
mpsz <- as_Spatial(mpsz_sf)
sg <- as_Spatial(sg_sf)

Display information of spatial class

st_crs(childcare)
Coordinate Reference System:
  User input: SVY21 / Singapore TM 
  wkt:
PROJCRS["SVY21 / Singapore TM",
    BASEGEOGCRS["SVY21",
        DATUM["SVY21",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4757]],
    CONVERSION["Singapore Transverse Mercator",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["northing (N)",north,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["easting (E)",east,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre, engineering survey, topographic mapping."],
        AREA["Singapore - onshore and offshore."],
        BBOX[1.13,103.59,1.47,104.07]],
    ID["EPSG",3414]]

3.1.1 Converting Spatial class into generic sp format

spatstat requires data to be in ppp object form Classes -> sp -> ppp

childcare_sp <- as(childcare, "SpatialPoints")
sg_sp <- as(sg, "SpatialPolygons")
childcare_sp
class       : SpatialPoints 
features    : 1545 
extent      : 11203.01, 45404.24, 25667.6, 49300.88  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
sg_sp
class       : SpatialPolygons 
features    : 60 
extent      : 2663.926, 56047.79, 16357.98, 50244.03  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 

3.1.2 Converting the generic sp format into spatstat ppp format

childcare_ppp <- as(childcare_sp, "ppp")
childcare_ppp
Planar point pattern: 1545 points
window: rectangle = [11203.01, 45404.24] x [25667.6, 49300.88] units
plot(childcare_ppp)

summary(childcare_ppp)
Planar point pattern:  1545 points
Average intensity 1.91145e-06 points per square unit

*Pattern contains duplicated points*

Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units

Window: rectangle = [11203.01, 45404.24] x [25667.6, 49300.88] units
                    (34200 x 23630 units)
Window area = 808287000 square units

3.1.3 Handling duplicated points

To check whether theres any duplicates:

any(duplicated(childcare_ppp))
[1] TRUE

To count number of co-indicence point, use multiplicity

# multiplicity(childcare_ppp)

To know how mnay locations have more than one point events:

sum(multiplicity(childcare_ppp) > 1)
[1] 128

There is 128 duplicated point events

tmap_mode('view')
tm_shape(childcare) +
  tm_dots(alpha = 0.4,
          size = 0.05)
tmap_mode('plot')

Ways to solve duplication problems: - 1. Delete duplicates – Cons: some useful point may be lost - 2. Use jittering: Adds a small perturbation so that duplicates do not occupy the same space - 3. Make each point unique

This code uses No.2 approach

childcare_ppp_jit <- rjitter(childcare_ppp,
                             retry = TRUE,
                             nsim = 1,
                             drop = TRUE)
any(duplicated(childcare_ppp_jit))
[1] FALSE

3.1.4 Creating owin object

Object called owin is designed to represent polygonal region.

sg_owin <- as(sg_sp, "owin")

plot(sg_owin)

summary(sg_owin)
Window: polygonal boundary
60 separate polygons (no holes)
            vertices        area relative.area
polygon 1         38 1.56140e+04      2.09e-05
polygon 2        735 4.69093e+06      6.27e-03
polygon 3         49 1.66986e+04      2.23e-05
polygon 4         76 3.12332e+05      4.17e-04
polygon 5       5141 6.36179e+08      8.50e-01
polygon 6         42 5.58317e+04      7.46e-05
polygon 7         67 1.31354e+06      1.75e-03
polygon 8         15 4.46420e+03      5.96e-06
polygon 9         14 5.46674e+03      7.30e-06
polygon 10        37 5.26194e+03      7.03e-06
polygon 11        53 3.44003e+04      4.59e-05
polygon 12        74 5.82234e+04      7.78e-05
polygon 13        69 5.63134e+04      7.52e-05
polygon 14       143 1.45139e+05      1.94e-04
polygon 15       165 3.38736e+05      4.52e-04
polygon 16       130 9.40465e+04      1.26e-04
polygon 17        19 1.80977e+03      2.42e-06
polygon 18        16 2.01046e+03      2.69e-06
polygon 19        93 4.30642e+05      5.75e-04
polygon 20        90 4.15092e+05      5.54e-04
polygon 21       721 1.92795e+06      2.57e-03
polygon 22       330 1.11896e+06      1.49e-03
polygon 23       115 9.28394e+05      1.24e-03
polygon 24        37 1.01705e+04      1.36e-05
polygon 25        25 1.66227e+04      2.22e-05
polygon 26        10 2.14507e+03      2.86e-06
polygon 27       190 2.02489e+05      2.70e-04
polygon 28       175 9.25904e+05      1.24e-03
polygon 29      1993 9.99217e+06      1.33e-02
polygon 30        38 2.42492e+04      3.24e-05
polygon 31        24 6.35239e+03      8.48e-06
polygon 32        53 6.35791e+05      8.49e-04
polygon 33        41 1.60161e+04      2.14e-05
polygon 34        22 2.54368e+03      3.40e-06
polygon 35        30 1.08382e+04      1.45e-05
polygon 36       327 2.16921e+06      2.90e-03
polygon 37       111 6.62927e+05      8.85e-04
polygon 38        90 1.15991e+05      1.55e-04
polygon 39        98 6.26829e+04      8.37e-05
polygon 40       415 3.25384e+06      4.35e-03
polygon 41       222 1.51142e+06      2.02e-03
polygon 42       107 6.33039e+05      8.45e-04
polygon 43         7 2.48299e+03      3.32e-06
polygon 44        17 3.28303e+04      4.38e-05
polygon 45        26 8.34758e+03      1.11e-05
polygon 46       177 4.67446e+05      6.24e-04
polygon 47        16 3.19460e+03      4.27e-06
polygon 48        15 4.87296e+03      6.51e-06
polygon 49        66 1.61841e+04      2.16e-05
polygon 50       149 5.63430e+06      7.53e-03
polygon 51       609 2.62570e+07      3.51e-02
polygon 52         8 7.82256e+03      1.04e-05
polygon 53       976 2.33447e+07      3.12e-02
polygon 54        55 8.25379e+04      1.10e-04
polygon 55       976 2.33447e+07      3.12e-02
polygon 56        61 3.33449e+05      4.45e-04
polygon 57         6 1.68410e+04      2.25e-05
polygon 58         4 9.45963e+03      1.26e-05
polygon 59        46 6.99702e+05      9.35e-04
polygon 60        13 7.00873e+04      9.36e-05
enclosing rectangle: [2663.93, 56047.79] x [16357.98, 50244.03] units
                     (53380 x 33890 units)
Window area = 748741000 square units
Fraction of frame area: 0.414

3.1.5 Combining point events object and owin object

Output combines the point and polygon feature in one ppp object class

childcareSG_ppp = childcare_ppp[sg_owin]

summary(childcareSG_ppp)
Planar point pattern:  1545 points
Average intensity 2.063463e-06 points per square unit

*Pattern contains duplicated points*

Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units

Window: polygonal boundary
60 separate polygons (no holes)
            vertices        area relative.area
polygon 1         38 1.56140e+04      2.09e-05
polygon 2        735 4.69093e+06      6.27e-03
polygon 3         49 1.66986e+04      2.23e-05
polygon 4         76 3.12332e+05      4.17e-04
polygon 5       5141 6.36179e+08      8.50e-01
polygon 6         42 5.58317e+04      7.46e-05
polygon 7         67 1.31354e+06      1.75e-03
polygon 8         15 4.46420e+03      5.96e-06
polygon 9         14 5.46674e+03      7.30e-06
polygon 10        37 5.26194e+03      7.03e-06
polygon 11        53 3.44003e+04      4.59e-05
polygon 12        74 5.82234e+04      7.78e-05
polygon 13        69 5.63134e+04      7.52e-05
polygon 14       143 1.45139e+05      1.94e-04
polygon 15       165 3.38736e+05      4.52e-04
polygon 16       130 9.40465e+04      1.26e-04
polygon 17        19 1.80977e+03      2.42e-06
polygon 18        16 2.01046e+03      2.69e-06
polygon 19        93 4.30642e+05      5.75e-04
polygon 20        90 4.15092e+05      5.54e-04
polygon 21       721 1.92795e+06      2.57e-03
polygon 22       330 1.11896e+06      1.49e-03
polygon 23       115 9.28394e+05      1.24e-03
polygon 24        37 1.01705e+04      1.36e-05
polygon 25        25 1.66227e+04      2.22e-05
polygon 26        10 2.14507e+03      2.86e-06
polygon 27       190 2.02489e+05      2.70e-04
polygon 28       175 9.25904e+05      1.24e-03
polygon 29      1993 9.99217e+06      1.33e-02
polygon 30        38 2.42492e+04      3.24e-05
polygon 31        24 6.35239e+03      8.48e-06
polygon 32        53 6.35791e+05      8.49e-04
polygon 33        41 1.60161e+04      2.14e-05
polygon 34        22 2.54368e+03      3.40e-06
polygon 35        30 1.08382e+04      1.45e-05
polygon 36       327 2.16921e+06      2.90e-03
polygon 37       111 6.62927e+05      8.85e-04
polygon 38        90 1.15991e+05      1.55e-04
polygon 39        98 6.26829e+04      8.37e-05
polygon 40       415 3.25384e+06      4.35e-03
polygon 41       222 1.51142e+06      2.02e-03
polygon 42       107 6.33039e+05      8.45e-04
polygon 43         7 2.48299e+03      3.32e-06
polygon 44        17 3.28303e+04      4.38e-05
polygon 45        26 8.34758e+03      1.11e-05
polygon 46       177 4.67446e+05      6.24e-04
polygon 47        16 3.19460e+03      4.27e-06
polygon 48        15 4.87296e+03      6.51e-06
polygon 49        66 1.61841e+04      2.16e-05
polygon 50       149 5.63430e+06      7.53e-03
polygon 51       609 2.62570e+07      3.51e-02
polygon 52         8 7.82256e+03      1.04e-05
polygon 53       976 2.33447e+07      3.12e-02
polygon 54        55 8.25379e+04      1.10e-04
polygon 55       976 2.33447e+07      3.12e-02
polygon 56        61 3.33449e+05      4.45e-04
polygon 57         6 1.68410e+04      2.25e-05
polygon 58         4 9.45963e+03      1.26e-05
polygon 59        46 6.99702e+05      9.35e-04
polygon 60        13 7.00873e+04      9.36e-05
enclosing rectangle: [2663.93, 56047.79] x [16357.98, 50244.03] units
                     (53380 x 33890 units)
Window area = 748741000 square units
Fraction of frame area: 0.414
plot(childcareSG_ppp)

3.1.6 First-order Spatial Point Patterns

3.1.6.1 Computing kernel density estimation using automatic bandwidth selection method

kde_childcareSG_bw <- density(childcareSG_ppp,
                              sigma=bw.diggle,
                              edge=TRUE,
                            kernel="gaussian") 
plot(kde_childcareSG_bw)

We can retrieve the bandwidth used to comput4e the kde layer

bw <- bw.diggle(childcareSG_ppp)
bw
   sigma 
298.4095 

3.1.6.2 Rescalling KDE values

childcareSG_ppp.km <- rescale(childcareSG_ppp, 1000, "km")

After rescaling, we can place it back through density()

kde_childcareSG.bw <- density(childcareSG_ppp.km, sigma=bw.diggle, edge=TRUE, kernel="gaussian")
plot(kde_childcareSG.bw)

3.1.6.3 Working with different automatic bandwidth methods

The code chunks below returns bandwidth using different methods

bw.CvL(childcareSG_ppp.km)
   sigma 
4.543278 
bw.scott(childcareSG_ppp.km)
 sigma.x  sigma.y 
2.224898 1.450966 
bw.ppl(childcareSG_ppp.km)
    sigma 
0.3897114 
bw.diggle(childcareSG_ppp.km)
    sigma 
0.2984095 

bw.ppl tends to produce more appropriate values when pattern consists predominantly of tight clusters bw.diggle is best when used to detect a single tight cluster in the midst of random noise

The code chunk below compares the plot output using bw.diggle and bw.ppl

kde_childcareSG.ppl <- density(childcareSG_ppp.km, 
                               sigma=bw.ppl, 
                               edge=TRUE,
                               kernel="gaussian")
par(mfrow=c(1,2))
plot(kde_childcareSG.bw, main = "bw.diggle")
plot(kde_childcareSG.ppl, main = "bw.ppl")

Code chunk compares kernel methods: Gaussian, Epanechnikov, Quartic, Dics

par(mfrow=c(2,2))
plot(density(childcareSG_ppp.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="gaussian"), 
     main="Gaussian")
plot(density(childcareSG_ppp.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="epanechnikov"), 
     main="Epanechnikov")
plot(density(childcareSG_ppp.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="quartic"), 
     main="Quartic")
plot(density(childcareSG_ppp.km, 
             sigma=bw.ppl, 
             edge=TRUE, 
             kernel="disc"), 
     main="Disc")

3.2 Fixed and Adaptive KDE

3.2.1 Computing KDE by using fixed bandwidth

kde_childcareSG_600 <- density(childcareSG_ppp.km, sigma=0.6, edge=TRUE, kernel="gaussian")
plot(kde_childcareSG_600)

#signma = 0.6 refers to bandwidth of 600meter or 0.6km

3.2.2 Computing KDE by using adaptive bandwidth

Fixed bandwidth method is very sensitive to highly skew distribution. To overcome this problem, use adaptive bandwidth: density.adaptive() of spatstat

kde_childcareSG_adaptive <- adaptive.density(childcareSG_ppp.km, method="kernel")

plot(kde_childcareSG_adaptive)

Comparing fixed and adaptive:

par(mfrow=c(1,2))
plot(kde_childcareSG.bw, main = "Fixed bandwidth")
plot(kde_childcareSG_adaptive, main = "Adaptive bandwidth")

3.2.3 Converting KDE output into grid object

gridded_kde_childcareSG_bw <- as.SpatialGridDataFrame.im(kde_childcareSG.bw)
spplot(gridded_kde_childcareSG_bw)

3.2.3.1 Converting gridded output into raster

kde_childcareSG_bw_raster <- raster(gridded_kde_childcareSG_bw)

kde_childcareSG_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.4170614, 0.2647348  (x, y)
extent     : 2.663926, 56.04779, 16.35798, 50.24403  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : v 
values     : -1.005814e-14, 28.51831  (min, max)

Notice that crs property is NA

3.2.3.2 Assigning projection systems

Codechunk below is used to include CRS information in kde_childcareSG_bw_raster

projection(kde_childcareSG_bw_raster) <- CRS("+init=EPSG:3414")

kde_childcareSG_bw_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.4170614, 0.2647348  (x, y)
extent     : 2.663926, 56.04779, 16.35798, 50.24403  (xmin, xmax, ymin, ymax)
crs        : +init=EPSG:3414 
source     : memory
names      : v 
values     : -1.005814e-14, 28.51831  (min, max)

Now, crs property is complete

3.2.4 Visualising the output in tmap

tm_shape(kde_childcareSG_bw_raster) +
  tm_raster("v") +
  tm_layout(legend.position = c("right", "bottom"), frame = FALSE)

3.2.5 Comparing Spatial Point Patterns using KDE

3.2.5.1 Extract the target planning areas

pg = mpsz[mpsz@data$PLN_AREA_N == "PUNGGOL",]
tm = mpsz[mpsz@data$PLN_AREA_N == "TAMPINES",]
ck = mpsz[mpsz@data$PLN_AREA_N == "CHOA CHU KANG",]
jw = mpsz[mpsz@data$PLN_AREA_N == "JURONG WEST",]
par(mfrow=c(2,2))
plot(pg, main = "Ponggol")
plot(tm, main = "Tampines")
plot(ck, main = "Choa Chu Kang")
plot(jw, main = "Jurong West")

3.2.5.2 Converting the spatial point data frame into generic sp format

pg_sp = as(pg, "SpatialPolygons")
tm_sp = as(tm, "SpatialPolygons")
ck_sp = as(ck, "SpatialPolygons")
jw_sp = as(jw, "SpatialPolygons")

3.2.5.3 Creating owin object

pg_owin = as(pg_sp, "owin")
tm_owin = as(tm_sp, "owin")
ck_owin = as(ck_sp, "owin")
jw_owin = as(jw_sp, "owin")

3.2.5.4 Combining childcare points and the study area

childcare_pg_ppp = childcare_ppp_jit[pg_owin]
childcare_tm_ppp = childcare_ppp_jit[tm_owin]
childcare_ck_ppp = childcare_ppp_jit[ck_owin]
childcare_jw_ppp = childcare_ppp_jit[jw_owin]

Use rescale() functions to change unit of measurement from m to km.

childcare_pg_ppp.km = rescale(childcare_pg_ppp, 1000, "km")
childcare_tm_ppp.km = rescale(childcare_tm_ppp, 1000, "km")
childcare_ck_ppp.km = rescale(childcare_ck_ppp, 1000, "km")
childcare_jw_ppp.km = rescale(childcare_jw_ppp, 1000, "km")
par(mfrow=c(2,2))
plot(childcare_pg_ppp.km, main="Punggol")
plot(childcare_tm_ppp.km, main="Tampines")
plot(childcare_ck_ppp.km, main="Choa Chu Kang")
plot(childcare_jw_ppp.km, main="Jurong West")

3.2.5.5 Computing KDE

Compute the KDE of these 4 planning areas. bw.diggle is used to derive bandwidth

par(mfrow=c(2,2))
plot(density(childcare_pg_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Punggol")
plot(density(childcare_tm_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Tempines")
plot(density(childcare_ck_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Choa Chu Kang")
plot(density(childcare_jw_ppp.km, 
             sigma=bw.diggle, 
             edge=TRUE, 
             kernel="gaussian"),
     main="JUrong West")

3.2.5.6 Computing bandwidth KDE

par(mfrow=c(2,2))
plot(density(childcare_ck_ppp.km, 
             sigma=0.25, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Chou Chu Kang")
plot(density(childcare_jw_ppp.km, 
             sigma=0.25, 
             edge=TRUE, 
             kernel="gaussian"),
     main="JUrong West")
plot(density(childcare_pg_ppp.km, 
             sigma=0.25, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Punggol")
plot(density(childcare_tm_ppp.km, 
             sigma=0.25, 
             edge=TRUE, 
             kernel="gaussian"),
     main="Tampines")

3.3 Nearest Neighbour Analysis

We will use the Clark-Evans test of aggregation for a spatial point pattern by using clarkevans.test() of statspat

The test hypotheses are:

H0 = The distribution of childcare services are randomly distributed H1 = The distribution of childcare services are not randomly distributed.

3.3.1 Testing spatial point patterns using Clark and Evans Test

clarkevans.test(childcareSG_ppp,
                correction="none",
                clipregion="sg_owin",
                alternative=c("clustered"),
                nsim=99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  childcareSG_ppp
R = 0.54756, p-value = 0.01
alternative hypothesis: clustered (R < 1)

4 Second-order Spatial Point Patterns Analysis

4.1 Analysing Spatial Point Process Using G-Function

G functions is used to measure the distribution of the distances from an arbitrary event to its nearest event. This is computed by using Gest() of spatstat package.

4.1.1 Choa Chu Kang planning area

G_CK = Gest(childcare_ck_ppp, correction = "border")
plot(G_CK, xlim = c(0,500))